Applied Agri-Technologies for Agriculture 4.0—Part I

Agriculture plays a vital role in the global economy, with much of the rural population in developing countries depending on it. The depletion of natural resources makes the improvement of agricultural production more important but also more difficult than ever [1]. In this context, effective interventions in agriculture are essential for the fulfillment of its vital role, which is to maintain rural social and economic stability within sustainable development. The extensive use of applied agri-technologies results in productivity improvement and better resource use and reduces the time needed for farm management, marketing, logistics, and quality assurance.

This Special Issue aimed to collect and present breakthrough research on various recent advances on processes in the field of applied agri-technologies. A total of 20 articles (including 14 original research papers and 6 review papers) are presented in this Special Issue, covering various fields in agriculture engineering discipline, including application in the agricultural production of:

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machine learning;

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automation and control;

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information technology;

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traceability, robotics;

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human–machine interaction;

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operations management;

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technology adoption.

Day-to-day farming operations include numerous tasks of detection, diagnosis, and prediction, providing a domain for a wide implementation of artificial intelligence and machine learning applications [2,3]. Kamarudin et al. [4] presents an extensive review of state-of-the-art methods for plant–water–stress estimations that implement the deep learning (DL) sensor fusion approach, along with future prospects and challenges in the application field. It is evident that the DL’s high learning capability is useful for the fast processing of field data originated from multi-source sensors. Furthermore, combining DL with an advanced data type such as 3D image can be considered for increasing this accuracy. However, sufficient datasets and variations of samples are required for creating robust methods. Anagnostis et al. [5] developed a robust convolutional neural network (CNN) model that is able to classify images of leaves, depending on whether or not these are infected by anthracnose. The best performing algorithm was compared with a series of state-of-the-art CNNs commonly used for image classification problems. The results saw that the developed algorithm for the problem performed equally, and in some cases even better, compared to these algorithms. Escamilla-García et al. [6] presents the applications of artificial neural networks (ANNs) in greenhouse technology, and how this type of model can be developed in the coming years by adapting to new technologies such as the internet of things (IoT). Different network training techniques are presented, where the feasibility of using optimization models for the learning process is exposed. The most important findings in this work can be used as guidelines for developers of smart protected agriculture technology, in line with the Agriculture 4.0 implementation.

In the area of information technology, Symeonaki et al. [7] focuses on the issue of facilitating the management, process, and exchange of the numerous and diverse data points generated in multiple Precision Farming environments by introducing a framework of a cloud-based context-aware middleware solution as part of a responsive, adaptive, and service-oriented IoT integrated system. As proof of concept, the functionality of the proposed system was studied in real conditions where some evaluation results regarding its performance were quoted.

The next group of articles regards applications of automation and control in modern agriculture. Hsiao et al. [8] investigated the application of a light-emitting diode (LED) lighting system to water bamboo cultivation during the nights of winter seasons. The proposed system is compared with previous LED systems, in which the LED bulbs were placed directly above water bamboo leaves, while in the proposed one, an LED array module was followed like the streetlight configuration. The results indicated that this lighting system can prevent the stunting of water bamboo leaves, further assist its growth, while it does not affect the harvesting process. Pardo-Alonso et al. [9] evaluated several alternatives in the graft assembly and coupling protocol. They studied the different working alternatives for grafting using a robotic system and analyzed two modes of joining. Additionally, they studied the different orientation alternatives for the cutting line and the seedling union with respect to the clip opening. Kim et al. [10] present the development of a prediction model for the tractor axle torque during tillage operation that can replace expensive axle torque sensors. A prediction model was proposed through regression analysis using key variables affecting the tractor axle torque. The prediction model was developed based on measured variables using multiple regression and was verified using the measured actual axle torque from field experiments. Rahim et al. [11] carried out a detailed study in order to evaluate the genetic distinction among Kurdish rice genotypes using the simple sequence repeats (SSRs) molecular markers and to perform in vitro tests to characterize the drought tolerance of six rice genotypes. The results saw that a significant genetic distance among the six rice genotypes for drought tolerance exists. Simakin et al. [12] presented a technology for obtaining photoconversion fluoropolymer films for greenhouses based on nanoscale fluorophores with a high quantum yield. Films could convert ultra-violet and violet radiation into the blue and red regions of the visible spectrum that is the most important for plant growth. It was shown that the use of photoconversion fluoropolymer films promotes the growth of biomass. Finally, Li et al. [13] presented a wheeled form trimming machine designed for shrub plants with the aim of reducing labor input, increasing efficiency, and improving trimming performance. To test the adaptability of the machine, five different shrub plants were chosen and trimmed by the machine. Results saw that the overall similarity was above 93%. Therefore, the form trimming machine developed could meet the requirements of shrub trimming in horticulture with desirable precision and adaptability.

In the area of agri-robotics, two research articles and a review are included. Zhang et al. [14] presented the development of an automatic navigation system for orchard vehicles based on 2D lasers in order to realize autonomous driving and path-tracking of vehicles in complex standardized orchards that involve much noise and interference between rows of fruit trees. The results saw that this study satisfies the precision requirements of orchard vehicle automatic navigation and provides guidance and suggestions for the industrial application of laser navigation with high precision and low cost in the standardized orchard planting industry. Edet and Mann [15] conducted a study to determine the visual requirements for a remote supervisor of an autonomous sprayer. Video sequences were used to obtain information about the boom status, the location and movement of the sprayer within the field, the weather conditions (especially the wind), obstacles to be avoided, crop conditions, and field conditions. Based on the outcomes of the work, the sprayer operators preferred the concept of visual information rather than information presented under graphical display using icons or symbols. Finally, in the review work, Moysiadis et al. [16] set out the theoretical foundation for understanding the planning aspects of mobile robots, particularly within the field of agriculture. A “narrative” review approach was adopted to first provide the basic terminology over the technical aspects of mobile robots used in autonomous operations, and then the fundamental planning aspects of mobile robots in agricultural environments were identified. This research aimed at supporting the planning of mobile robots in agriculture through identifying and classifying a set of technical terms and basic planning attributes.

In field operations, “field efficiency” is a time-based measure for the productivity of an operational system directly connected with the economic performance of the operating system [17]. Zhou et al. [18] presented a new, more objective and measurable index for the field efficiency based on the travelled distance of an agricultural machinery during a field operation, the field traversing efficiency (FTE), as a function of well-quantified operational specifications. To show the degree of the dependence of the FTE index on the operational features, 864 scenarios derived from the consideration of 6 sample field shapes, 3 conventional fieldwork patterns, 4 driving directions, and 12 combinations of machine unit kinematics and implement width were evaluated by the developed tool. The test results showed that variation in FTE was up to 23% in the tested scenarios when using different operational setups. This approach on measuring field efficiency is very important for the evaluation of advanced operational approaches on field operations planning [19,20].

As it has been considered by the agri-robotics research community, an intermediate step before the implementation of fully autonomous systems in the field is the cooperating and synergetic systems between human and robots [21,22]. However, to understand and be prepared for such applications, existing knowledge on the connection between machine and human workers in agricultural operations must be reported and analyzed. In this Special Issue, two review papers are dedicated to ergonomic issues in field operations. Benos et al. [23] presented a systematic review regarding the identification of the key risk factors associated with musculoskeletal disorders (MSDs) coming from manual agricultural operations. Additionally, the main root causes were determined along with the evaluation of the current ergonomic interventions. As a second part of this work, Benos et al. [24] performed a review of the recent scholarly literature on ergonomics in agricultural mechanized operations. The results demonstrated that ergonomics in agriculture is an interdisciplinary topic where more collaborative efforts among physicians, ergonomists, engineers, and manufacturers are necessary for creating new ergonomic technologies and increasing the awareness of workers for the involved risk factors. Regarding targeted applications of human–machine cooperation in agriculture, Anagnostis et al. [25] presented a methodology to properly identify human activities related to the task of lifting a crate and placing it onto a robot suitable for agricultural operations with the use of machine learning algorithms for sequential data classification. The proposed methodology demonstrated considerable efficacy in predicting the defined sub-activities with an average accuracy of 85.6%. These results confirmed the applicability of the proposed methodology for human awareness purposes in agricultural environments.

In the area of agri-food chains and related traceability issues, Demestichas et al. [26] presented an extensive literature review on the integration of blockchain into traceability systems. Initially, the review presented definitions, levels of adoption, tools, and advantages of traceability systems, accompanied by a brief overview of the functionality and advantages of blockchain technology. An extensive literature review on the integration of blockchain into traceability systems was conducted while existing commercial applications are presented, highlighting the relevant challenges and prospects of the application of blockchain technologies in the agri-food supply chain. Garrido-Izard et al. [27] presented an application of monitoring the ear skin temperature (EST) of pigs during commercial intercontinental transport. They used the phase space methodology to characterize the EST of animals during different stages of a long commercial journey, including intercontinental air transport. Phase space areas were significantly higher for all the animals during air travel, almost doubling that of road transport.

Finally, and by covering aspects of technology adoption, Vlontzos et al. [28] underlined the driving factors of farmers’ engagement in Participatory Research Projects (PRPs). This is a critical issue for formulating efficient and effective technology transfer channels, essential for improving the operational status of agricultural holdings. The results show that Farmers’ Willingness and Social Influences are the factors that mostly affect their decision to engage in a Participatory Research Project (PRP).

As mentioned before, this Special Issue covers various aspects of agri-technology, ranged from machine learning applications up to issues regarding their adoption by farmers. Despite the coherent and wide range of applications listed here, it is evident that not all topics can be covered by any single work. However, it is important for the scientific community of agricultural engineers to understand the necessity for applied approaches that provide solutions on critical matters and processes within agricultural production and, in parallel, are convincing for their implementation from the stakeholders of this production system. This is the sole way for the prospects of agriculture 4.0 to be realized.